I’ll be writing more frequently and the topics will be broader than economic development and Austin, veering more into politics and my continued interest in how data and information are politicized. Call it our Politicized Economy. Looking forward to your comments.

I gave an update on the Austin economy today at the Austin CPA Chapter CFO/Controllers Conference. It was good timing, as last week the U.S. Census Bureau published new data from the 2015 American Community Survey to round out the other usual sources.

44 consecutive months of 4.0%+ total nonfarm job growth on a year-over-year basis came to an end in April. So we’ve come out of ludicrous speed and are now cruising at a comfortable ridiculous speed, more or less where we’ve been in non-recession years since 2004. Austin has become so accustomed to this level of growth that we often take it for granted–no other major metro in the country has come close to 44 consecutive months at 4.0% or better.

Total real personal income in the Austin metro grew 22.5% during 2010-2014, ranking third behind Houston (24.2%) and San Jose (22.5%) among large metros. That’s equivalent to adding $15.6 billion in real income to the regional economy–quite a bit of fuel for our housing market and increasingly pricey foodie scene.

Austin is attracting and retaining so many well-educated and highly-paid people that we almost have to stop measuring in terms of bachelor’s degrees and start counting the advanced degrees. Austin (city) added nearly 30,000 residents with graduate degrees during 2010-2015, with median earnings of $70,000 per year.

Austin’s tech sector continues to lead nationally, with total employment growing 31% during 2010-2015, fourth highest among major metros with at least 50,000 tech jobs. Travis County ranked fifth among counties. Mobile, apps, and SaaS companies are driving a significant portion of the growth, with employment in Travis County up 115% during that five-year period (#2 nationally behind SF).

We also touched on the usual three challenges:

Inclusive economic development. 45% of primary working age (25-64) residents in Austin do not have a completed postsecondary degree. 67% of Black residents and 75% of Hispanic or Latino residents age 25+ do not have a completed postsecondary degree, compared to 22% of Asians and 31% of Whites. Median gross rent for a 2BR in Austin (city) is up to $1,243 per month, exceeding what is considered affordable for the housing budgets of Hispanic or Latino and Black families with median family income, as well as the budget at median earnings for all workers age 25+ who have less than a bachelor’s degree. This is how you become one of the most economically segregated places in the country.

When will housing costs in the urban core start to erode Austin’s competitive advantage relative to the expensive coastal markets? Affordability–relative to pricey markets with comparable economies–has long been one of Austin’s key selling points. There is still a considerable gap relative to California, but are we approaching a point on the curve where some people will opt for smaller markets with growing tech centers in North Carolina, Tennessee, or Utah?

Trying to tackle big-city/regional issues with a small-city/local toolkit, and, at times, mindset. $6 billion transit plan in Nashville. Multi-billion dollar investment in Denver approved by voters in eight counties. Meanwhile, we are squabbling about seats on our MPO and protesting $720 million in relatively small-scale upgrades–in one city.

Austin is great at many things, but you would think with all those graduate degrees we’d be able to show more progress on regional collaboration.

Well if you’re going to get scooped on a story, at least let it be by somebody you read and respect, with better data.

Jonathan Rothwell, formerly at Brookings and now a senior economist at Gallup, has published a working paper, Explaining Nationalist Political Views: The Case of Donald Trump, which sorts through many of the questions I raised in my first piece about the 2016 Election. The Washington Post published a summary of Rothwell’s findings, but I recommend reading the paper itself, as qualifiers usually don’t come through clearly enough in mainstream media coverage of academic research.

Rothwell used Gallup Daily Tracking survey microdata from approximately 87,000 interviews conducted between July 2015 and July 2016, in which American adults were asked how favorably they viewed Trump, as well as a series of identifiers, such as political views and party affiliation, race/ethnicity, educational attainment, occupation, and more. Rothwell then cleverly linked the responses to sub-county level geographies to compare with Raj Chetty’s economic mobility data.

Rothwell’s use of Gallup survey data gets around many of the primary vote data limitations I discussed in my piece. For example, Rothwell was able to directly examine the relationship between a respondent’s view of Trump and his or her socioeconomic and demographic characteristics, rather than having to infer connections, as I did, based on what a county, as a whole, looks like and how residents of that county voted, in the aggregate. Rothwell’s analysis provides a degree of precision that is not possible using county-level vote totals and Census data. Further, Gallup data made it possible for Rothwell to apply weights to the sample to make it nationally representative.

There are many interesting aspects of Rothwell’s analysis, but the key passage is on p. 11 of the paper:

“These results do not present a clear picture between social and economic hardship and support for Trump. The standard economic measures of income and employment status show that, if anything, more affluent Americans favor Trump, even among white non-Hispanics. Surprisingly, there appears to be no link whatsoever between exposure to trade competition and support for nationalist policies in America, as embodied by the Trump campaign.”

The reference to trade competition is in response to a popular argument that Trump’s support is fueled by workers–specifically, white, male workers–in economically distressed communities tied to long-term stagnation or decline in manufacturing and other “blue-collar” employment, a perceived impact of trade liberalization. Rothwell’s analysis found no such evidence, when controlling for demographic characteristics, party affiliation, etc.

And later (p. 12):

“. . . this analysis provides clear evidence that those who view Trump favorably are disproportionately living in racially and culturally isolated zip codes and commuting zones. Holding other factors constant, support for Trump is highly elevated in areas with few college graduates, far from the Mexican border, and in neighborhoods that standout within the commuting zone for being white, segregated enclaves, with little exposure to Blacks, Asians, and Hispanics.”

There’s plenty to debate in the paper–check out twitter for scholarly disagreement about model specification, robustness, and multicollinearity–but I don’t find much that’s debatable in Rothwell’s conclusions, based on my analysis of the county-level presidential primary returns, flawed as they may be as a data set. In fact:

White Alone, Not Hispanic or Latino share of total population and educational attainment among White Alone, Not Hispanic or Latino males age 25 or older are statistically significant predictors of Trump’s share of the total primary vote at the county level, consistent with Rothwell’s findings.

Here is the urban-rural split of Trump’s share of the total primary vote compared to Clinton and Sanders for counties included in my data set where White Alone, Not Hispanic or Latino residents make up 50% or more of total population and 50% or more of White Alone, Not Hispanic or Latino males age 25+ have no college:

And, to Rothwell’s point about contact theory (pgs. 8-9, 12), here is the same table as above, but this time showing counties where White Alone, Not Hispanic or Latino residents make up 90% or more of total population and 65% (mean + 1 SD among majority white counties) or more of White Alone, Not Hispanic or Latino males age 25+ have no college:

Only 200 counties, but go back and compare these tables to the one in my last piece and you’ll find that my analysis is generally consistent with Rothwell’s findings.

Using data from EMSI, which provides estimates of total employment by industry for small counties where QCEW data is suppressed, I can find no statistically significant relationship between Trump’s share of the total primary vote and any measure of manufacturing employment I could think of testing, including the industry’s current and past shares of total employment in the county, change in industry employment over various time periods, or change in number of male workers in the labor force relative to manufacturing jobs available.

Again, generally consistent with Rothwell’s findings.

There are some interesting exceptions and regional differences, especially when comparing Trump to the other two main candidates. I’ll get into that next time.

We like to take the occasional detour into politics, especially when there’s an economic development story to be told. In the past we’ve looked at Mitt Romney’s infamous makers and takers argument, Rick Perry’s Texas Miracle, and creative class support for Obama. Successful politicians, our storytellers-in-chief, are particularly adept at turning immensely complicated issues into palatable soundbites because complexity and uncertainty have a way of stoking demand for easy answers–the convenient truths–that shape the narrative, as they say.

Sloganizing complex forces shaping voter perceptions of the economy and their place in it is hardly a new political tactic, but for those of us who pay attention to rural economic development and labor market issues, 2016 seems to be a marked departure from the usual talking points. The 2004 election made “offshoring” of jobs somewhat of a national issue, but I don’t recall much of an urban vs. rural flavor to the debate. The red state/blue state narrative, articulated beautifully by local journalist Bill Bishop in The Big Sort, certainly included economics, but was really more of a statement about social and cultural factors–an epilogue, of sorts, to the culture wars of the 1980s and 1990s. Indeed, we’d still be talking about how Tim Russert broke the Internet on election night if Twitter were around in 2000.

Politicians and pundits love to characterize every presidential election as a critical inflection point, the proverbial crossroads that represents some deliberate attempt on the part of the electorate to choose a distinct path forward. Most elections probably don’t live up to that billing with the benefit of hindsight. We’ll see what the historians say about 2016. But the convenient truths of the 2016 presidential election are not hard to spot, and our ability to sort out what’s real from what’s being used as political cannon fodder may help determine economic and labor market policies affecting Rural America in the next administration.

I’ve been combing through results of the presidential primaries, and, through that process, developed a new appreciation for people who do that for a living. Speaking of, I need to thank Joshua Darr, assistant professor of political communication at LSU, for the pointer to Ben Hamner’s election data warehouse on Kaggle. Most of the major news outlets host interactive maps of election returns, but for obvious reasons don’t make it easy to assemble your own data sets to work with.

There are several caveats to keep in mind when working with primary data. First, primaries are not reliable predictors of turnout for the general election. Turnout for the primaries this year was high by historical standards, approaching the record participation in 2008, but turnout for the general in November could throw off conclusions drawn from primary results in any number of ways.

In addition, the way in which primary elections are held and results are reported in several states make it challenging to assemble a complete and accurate national data set. The majority of states report primary results at the county level, making it easy to match returns to other data sources, such as the American Community Survey. However, some states report results using different sub-state geographies that do not line up nicely with counties, such as congressional districts or townships, or, in a few cases, not at all (e.g., Republicans in Colorado).

So, with apologies to AK, CO, CT, KS, ME, MA, MN, ND, RI, VT, WY, as well as the District of Columbia, I’m going to use a data base of primary results in 2,720 counties, matched to various data from the 2014 ACS 5-Year Estimates, to make a few observations about the 2016 presidential election over the course of the next few weeks. Given the states omitted from the analysis, combined with the inherent shortcomings of primary data, the purists among you will no doubt be left wholly unsatisfied. But we work with what we have. Finally, two pieces of background reading I want to mention because they motivated me to take this on:

The overarching narratives I want to explore deal with race/ethnicity and education characteristics of voters supporting the two nominees, Hillary Clinton and Donald Trump, although Bernie Sanders will make a few appearances, as well. In particular, I’m interested in this idea that non-college, working-class whites, reacting, in part, to declining economic prospects, are breaking for Trump in a significant way, and whether or not there are differences between urban and rural counties.

But before we can get to any of that, we need a basic understanding of how people voted in urban and rural counties. The USDA Economic Research Service classifies counties on an urban-rural continuum. The first three rows in the table below are counties located in metropolitan statistical areas (MSAs). Rows four through nine are non-MSA counties, with rows eight and nine representing counties USDA ERS considers “completely rural.” Here’s a breakdown of the popular vote for Clinton, Trump, and Sanders, on that urban-rural continuum:

Approximately 60 million votes were cast in the primaries, which means we have about 87% represented in our data set. Trump received 45% of the Republican primary vote and 23% of the total popular vote. Clinton got 55% of the Democratic primary vote and 28% of the total popular vote. So the county data set we’re using here with 11 states and DC missing is tilted by about one percentage point in favor of Trump, and it short changes Sanders by about two percentage points (he won 22% of the total popular vote). Keep that in mind as we continue this thread, but it shouldn’t have too much bearing on the themes we’ll discuss.

As always, feel free to fire away with questions or comments and I’ll try to work them in. Thanks for reading. More soon.

The majority of people moving to Travis County are now coming from other states, according to new data from IRS.

An estimated 265,000 people moved to Travis County from other states in 2011-2014, third-highest among counties behind Maricopa (Phoenix) and Los Angeles. On its own, that doesn’t sound all that exciting, or surprising. During periods of strong economic growth big places tend to attract a lot of people, especially from other big places, and Austin is experiencing the best economy in a generation.

As others have pointed out, several Texas counties are among the leaders nationally in attracting new residents from other states. So why, then, does it “feel” different in Austin compared to other high-growth places? Why does it seem like there are so many more out-of-state transplants here fueling population growth? Confirmation bias in the form of staring at Florida or California license plates while sitting in traffic or getting outbid on a house from an “out-of-state buyer paying cash” may have something to do with it. But there is ample evidence in the recently released IRS data to suggest that perceptions reflect demographic reality.

Five Texas counties ranked among the top twenty counties nationally in number of movers from other states in 2011-2014: Travis (#3), Harris (#5), Dallas (#10), Tarrant (#14), and Bexar (#16). But of that group, out-of-state movers made up a clear majority (66%) of total domestic movers in only Travis County, i.e. 34% of people moving to Travis County from somewhere else in the US came from some other county in Texas. Bexar County (San Antonio) and Harris County (Houston) were about evenly split between in-state and out-of-state, but Dallas County (62% in-state) and Tarrant County (60% in-state) tipped strongly in the other direction. Same goes for Williamson, Collin, Denton, Fort Bend, and most other large, fast-growing suburban counties around the state.

Of the 24 counties on the receiving end of 100,000 or more out-of-state movers in 2011-2014, five stand out as outliers, with (1) out-of-state movers making up 60% or more of total movers into the county; and (2) out-of-state movers (summed 2011-14) representing a relatively large share (10% or more) of total residents:

Travis County, Texas (Austin) – total out-of-state movers in 2011-14 were 23% of total residents, by far the greatest concentration among large counties with the most out-of-state movers and nearly all counties of any size with significant military presence, a key driver of out-of-state migration.

Mecklenburg County, North Carolina (Charlotte) – among fastest growing places in US, a major finance center, located on the border of two states.

Out-of-state movers to Travis County outnumbered out-of-state movers to 24 states and Washington DC, and came within 1,000 movers, or 0.4%, of Oklahoma.

New migration data always makes a big splash, as politicians are quick to take credit (or deflect blame), and several moving companies have gotten into the game lately. For Austin, it’s yet another reminder that you are living in the best economy you’re likely to experience–that is, if you are able to keep up.

It’s upon us. The time of year locals love to hate and hate to love. When the “old Austin” vs “new Austin” bickering reaches its fever pitch crescendo, increasing to new levels of absurdity each year.

When usually reasonable minded people convince themselves that live tweeting panels somehow counts as productive economic activity.

When the Panel Economy, as a friend so aptly put it recently, blurs the line between what’s real and worth paying attention to and what’s personal brand marketing, which I’m still not really sure how to define or make sense of.

Economic development outfits from cities around the US have apparently joined the party like never before this year in an attempt to convince SXSW intelligentsia that their markets are viable alternatives to the usual suspects. So, in the spirit of promoting fair competition, here’s a quick update on 2015 performance and 2016 projections for some of the tech-driven regional economies around the US.

We’re defining tech here in the same way we do for the research we’ve published with the Austin Technology Council, relying primarily on the CompTIA/TECNA definition used in their annual Cyberstates report, but with an additional industry category that captures one of Austin’s largest tech employers. Our adapted CompTIA/TECNA definition includes 49 six-digit NAICS industries, and we rely on data from EMSI, which includes self-employment.

The first table below focuses on metropolitan statistical areas (MSAs) with at least 50,000 employees in the tech sector and a location quotient of at least 1.5, or, in other words, places where the concentration of tech employment is at least 50% greater than the US economy as a whole.

Economic development analysts like to quibble over where to draw that line–1.5, 1.3, 1.2–to delineate degrees of concentration, but for our purposes here 1.5 is good enough because it captures most of the usual suspects and excludes markets like New York (0.9) and Los Angeles (1.0) that have large tech sectors simply because they are very large markets overall with a lot of employees in most sectors.

Atlanta, you have a strong case (1.36) to argue while you’re here, but we’re leaving you off this list.

As expected, San Jose and San Francisco are leading the pack of large and highly concentrated tech markets ranked by job growth in 2015. Raleigh and Portland, with fewer than 100,000 tech employees, get a bit of a boost here since we’ve used percentage growth to create the ranking, but impressive nonetheless. I’m a bit surprised that Austin is not a few spots higher on the list, but 3.4% annual job growth is nothing to be ashamed of. EMSI’s projections for 2016 seem to reflect the slowdown that many of the macro soothsayers have been predicting now for several years, with only San Francisco and Seattle in the 3.0%+ range.

Austinites love to complain about how the city is turning into California, even though much of that ire should really be directed at Florida. But for those of you in Austin worried about becoming the “next Silicon Valley,” don’t fret, we have a really long way to go. Value-added is the tech sector’s contribution to Gross Domestic Product (GDP) at the regional level. Basically, it is tech’s share–direct, no multiplier–of the total regional economy. Tech accounts for $98.3 billion of San Jose MSA’s GDP, or approximately one-half of the total, according to EMSI’s estimates. That’s nearly as large as Austin MSA’s entire GDP ($107.7 billion). Further, Austin’s economy is much more diversified than Silicon Valley’s economy. Tech here makes up only about 21% of Austin MSA’s GDP.

However, if anybody sees Mike Judge at SXSW, this in no way suggests that he shouldn’t do a Silicon Valley spinoff on the social entrepreneurship scene in Austin. Mr. Judge, you should totally do a Silicon Valley spinoff on the social entrepreneurship scene in Austin. #socent

Now, the smaller markets:

We’re defining small here as highly concentrated (tech LQ 1.5+) MSAs with 10,000-50,000 tech employees. Two of my favorites, Provo and Durham, make the cut, as well as a few others I know very little about (Palm Bay?). Provo’s economy appears to be at ludicrous speed, with merely ridiculous speed projected for 2016.

My advice to the economic developers and PR professionals, especially from non-coastal markets, in town for SXSW marketing their cities: print out a copy of these tables, add a column with average housing costs, and then make your pitch. We’re reaching a tipping point, even here in Austin.

Unemployment in Austin (city and MSA) is down to its lowest level since the dot-com era. With job growth of 3.0%+ annually and historically low unemployment, we’re finally seeing some upward movement in wages, at the average at least. In fact, it took fifteen years, but average wages in Austin are finally back to pre-dot-com recession levels.

Total employment in the tech sector in Austin has surpassed peak employment of the dot-com era. Tech companies in Austin added more than 23,000 jobs between 2010 and 2015, a 27% growth rate that was nearly double the growth rate of other leading Texas markets and well above most peer markets around the country, with the exception of San Francisco (42%) and San Jose (31%). Manufacturing employment in the tech sector appears to have stabilized for now, and Austin’s IT Services and Applications industry (NAICS 5415, for data wonks), is among the highest performing markets nationally, doubling in employment since 2010.

A few other thoughts from today:

There is still a great deal of uncertainty about how sustained lower oil prices will impact Austin, relative to what’s going on in the rest of Texas. Eldon brought up a good point today about underperforming assets in Houston and what that might do to availability of capital and real estate investment in Austin. Discretionary spending in Austin could also take a hit, since oil and gas holdings have generated a lot of income for Austin residents holding them over the last few years; same goes for the pricier end of the housing market, I suppose.

But I still think Austin’s biggest exposure is how the state budget may be impacted. State government accounts for about 7.5% of total employment in Austin, more than 70,000 jobs. If sustained lower oil prices start to translate to budget cuts and staff reductions, then we’re going to feel it. So while lease holders, investors, and people working for oil and gas related firms in Austin may be having a different experience, there’s no evidence that lower oil prices are having much of an impact on the Austin labor market, at least not yet.

EMSI projection for job growth in 2016 is 3.1%, with tech at 2.4% (3.1% services, 0.4% manufacturing). The consensus forecast for Austin at this time last year was in the 2.0%-2.5% range, with most forecasting firms expressing a great deal of uncertainty about how the oil and gas market would impact Austin. With a year of data now available in the context of lower oil prices and slower statewide growth, perhaps the forecasters are a bit more confident of Austin’s resilience in the face of what’s happening in the rest of Texas. We’ll see.

Finally, what would a forecast be without a new year’s resolution? If you look at only one slide from my presentation today, please make it Slide 13. It’s about as plain as I can make it in terms of how educational attainment, income disparity, and housing costs are painting a discouraging portrait of economic segregation and inequality of opportunity in Austin.

We hear a lot about traffic these days from our elected leaders in Austin and around the region. Yet, it’s the one issue we can do the least about. Traffic is an inevitable consequence of economic growth, and until people make different decisions about where and how they want to live–contingent on having more affordable housing and better transit available to give people that choice of living differently–traffic is going to get worse. Dispensing with the calls for “fixing” traffic or employing an “all of the above” approach to transportation would be a great way to start 2016. I realize it’s good politics, but it distracts from the real trade-offs we need to grapple with.

Instead, let’s focus on something we can do something about: empowering more people with education and training they need to fully participate in Austin’s dynamic, growing economy. Increasing the number of high schools offering dual credit so students have the opportunity to earn a postsecondary degree–the prerequisite for having a chance to keep up with rising cost of living in Austin–before graduating and leaving home. Taking a close look at the positive impact of family resource centers (disclosure: I’m on the TAP board) and the community school model as stabilizing factors in areas undergoing rapid change.

Investing in neighborhoods willing to innovate, something we profess to love so much in this town.

Austin isn’t alone, of course, in facing challenges related to economic segregation. However, the astounding influx of wealth (Slide 11), high-wage job creation (Slide 7), and rapidly increasing housing costs are putting a finer point on it here, especially compared to peer markets like my hometown area, Raleigh-Durham. Given the limited tools and resources at the disposable of cities and counties in Texas, we’re not going to “fix” any of these challenges, at least not locally. But we can make progress toward measurable goals, if we’re willing to be serious about it.

A few thoughts on Austin, economic development, and Civic Analytics as we close out 2015:

Austin

Uncertainty about the state of the Texas economy looking down the barrel of sustained lower oil prices led some prognosticators toward the end of 2014 to forecast “moderating” growth in Austin during 2015, which for us is somewhere in the 2.0%-2.5% range. Well, as the great economist John Kenneth Galbraith once said, “The only function of economic forecasting is to make astrology look respectable.” Total employment in the Austin-Round Rock MSA is up by 34,000 jobs through November (3.7%), compared to 26,600 jobs (3.0%) this time last year, according to the latest report from the Texas Workforce Commission. We’re slightly off pace compared to 2013 (4.1%) and 2012 (4.5%), but, barring something very strange turning up in the December report, we are going to finish the year well north of 2.0%-2.5%, proving, yet again, that there are better ways to spend your morning than listening to economists feebly attempt to predict the future. Texas, meanwhile, stands at 1.1% for the year.

Make sure to look for Dan Zehr’s summary of 2015 in the Statesman. Dan’s been on top of the latest data releases all year, providing helpful context with interviews and interesting longer-form stories. We’re lucky to have him.

But despite Austin’s enviable position at the top of most “best of” lists, equitable or inclusive economic development continues to elude us. This is not a challenge unique to Austin, of course, but our population growth rate and influx of higher income households magnify the challenge. We got a few stark reminders this year with new data from the U.S. Census Bureau. First, there’s this thoroughly depressing map of concentrated poverty among children using new data from the 2014 ACS five-year estimates. Darkest shaded areas are 50%+ children in poverty.

Wages have been trending upward, at least at the average, but there is still wide disparity among race/ethnicity groups in Austin. Average earnings for Hispanics (69%) and Blacks (65%) are less than 70% of what Whites are earning in Travis County. The last time that ratio exceeded 70% was nearly 20 years ago. Average earnings (wages, salaries, self-employment income; no benefits) for Whites in Travis County are $60,932 per year, compared to $41,332 for Hispanics and $39,620 for Blacks. To put that into perspective, using the affordability guideline of no more than 30% of income spent on housing costs, Black workers in Travis County, on average, can afford to spend about $991 per month on housing costs (rent or mortgage, utilities, etc.), compared to $1,523 per month for Whites. With average rents of more than $1,000 per month across most of central Austin, combined with no significant improvement in wage/income inequality among race/ethnicity groups, we’ll continue to struggle as a community with the pernicious effects of economic segregation into 2016 and beyond.

We also got a fresh look at The Human Capital with new ACS data on educational attainment. Much of Austin’s (city) transformation over the last ten years can be attributed to growth in the very well educated, very high income part of the population. ACS (one-year) estimates indicate that Austin added more than 39,000 people age 25 or older with a graduate degree between 2006 and 2014. That’s 53% growth in Austin’s graduate degree population in less than ten years. Put another way, for every four people age 25 or older that Austin gained between 2006 and 2014, one of them had a graduate degree.

For context, compare Austin to two outliers, San Francisco and Washington DC, in terms of highly concentrated, very well educated, very high income populations, and then one peer city in Raleigh, NC (my hometown). Austin’s graduate degree holding population growth rate of 53% during 2006-2014 exceeded Raleigh (42%), Washington DC (41%), and San Francisco (32%). Numeric growth is even more telling. Austin’s numeric growth of 39,197 graduate degree holders during 2006-2014 was more than San Francisco (34,409), more than double Raleigh (14,393), and nearly as many as the most extreme outlier, Washington DC (41,322).

Austin’s dramatic increase in very well educated population means, of course, an equally dramatic increase in very high income households. Median earnings for Austin (city) residents age 25 or older with a graduate degree are $63,089 per year. Average earnings are more than $90,000, based on residents of Travis County age 25 or older with a bachelor’s degree or better (city-level, graduate degree specific data not available). If we assume that people with a graduate degree earn closer to the average than the median, that’s a potential gain of about 39,000 people earning $90,000+ per year added to the city’s population in only eight years, 2006-2014. Indeed, according to Census estimates, Austin (city) gained 13,770 households with income of $150,000 or more during 2006-2014, a 41% growth rate, compared to Washington DC (40%), San Francisco (28%), and Raleigh (34%).

[Sidebar: People ask me sometimes when they find out I’m from Raleigh why Austin and Raleigh “feel” different, despite experiencing similar rates of population and economic growth. I suspect there are many explanations for that, but one of the main differences is earnings. Numeric growth of $150,000+ income households in Austin was nearly three times that of Raleigh during 2006-2014, and average earnings for bachelor’s+ residents in Austin are considerably higher than Raleigh, $93,480/year versus $79,416/year. This may partially explain why Austin “feels” different than Raleigh in terms of housing costs, foodie scene fueled by discretionary income, etc.]

The question facing The Human Capital now is at what point housing costs in Austin rise high enough to discourage even the very well educated, higher income population from moving to and/or staying in Austin, especially given the increasing number of smaller markets that offer many of the same amenities at a much lower price tag (e.g, Durham, Grand Rapids, Chattanooga). Austin is still a bargain compared to outliers like San Francisco and Washington DC, but we need to be cognizant of how far we’ve diverged from traditional points of comparison like Raleigh. Trust me, economic developers in Raleigh-Durham, Charlotte, Nashville, and other peer markets are well aware of it.

The new CEDS guidelines from EDA provide unprecedented flexibility to economic development districts (EDDs) in terms of how they create and implement a regional strategy. Many EDDs are already taking advantage of it, exploring new formats, leveraging web-based data platforms, and using the CEDS as a vehicle to lead coordinated, integrated regional planning efforts. Yet, we still lack a proper compliance mechanism–the proverbial carrot or stick–to ensure that EDDs take advantage of this “opportunity to excel,” as one of my former bosses liked to put it. I had hoped that the Jobs Accelerator program, or one of the other EDA-led inter-agency initiatives, could be used as an incentive to provide more funding to entrepreneurial EDDs embracing new and innovative approaches to the CEDS, but that hasn’t happened so far.

While there are many EDD leaders, in the NADO membership and elsewhere, who can be spotlighted as a way to encourage others, without sufficient carrots and/or sticks we shouldn’t assume that the much-improved CEDS guidelines will result in widespread, immediate improvement. We’ll be doing our part to contribute to the state of practice through continued work with NADO, as well as kicking off the first-ever regional CEDS in Sonoma County and Mendocino County, CA, in early 2016.

Civic Analytics

It’s been a fun year. What started out as a one-man economic development consulting shop in 2012 has grown into a multi-disciplinary team engaged in projects that I never would have imagined three years ago.

Most important, Meredith and Isabelle joined the team. We’re lucky to have them.

Civic Analytics has never been a traditional planning firm, in economic development or any other field. While every project shares a common theme–using data and technology to help community leaders make informed decisions–we’ve evolved from writing plans and reports for clients to helping clients build organizational capacity to improve the way planning is done, and develop the skills and experience to put plans into action themselves. We’re glad to help clients create a plan, but we would much rather help create better planners.

There are 387 federally-funded Economic Development Districts (EDDs) across the country. These EDDs, operating with planning funds from the U.S. Economic Development Administration (EDA), manage a wide array of multi-county, regional programs related to community and economic development. Occasionally, one of these EDDs will win a large, multi-million-dollar, multi-year grant from a program like HUD Sustainable Communities and then create an impressive data platform that makes integrated regional planning a lot easier. Urban planners can overlay project information, such as proposed transportation improvements or affordable housing developments, over base maps that demonstrate need with demographic data. Economic developers can showcase available properties and incentive offerings on maps that identify workforce availability around any given site. Many EDDs are investing in dashboards that feature compelling visualizations of economic indicators or plan performance measures.

Examples of impressive data platforms are not hard to find in the planning world. The challenge is: What happens when the grant money runs out?

Most EDA-funded Economic Development Districts operate on very lean budgets, especially those located in small metropolitan and rural communities. Licensing even the most basic GIS and/or data platform solutions from private firms is out of the question for the vast majority of EDDs. Further, value-added services at EDDs, or any regional planning organization with voluntary participation from local governments, must be financially sustained through fee-for-service or cost sharing agreements with members. Getting buy-in from members to purchase and maintain these tools (not to mention hire and train the planners to run them) requires a very clearly communicated return on investment. Data platforms, GIS tools, and dashboards must have functional value to planners and other technical users, but be easy enough for non-technical, general public users to access in order to create a wide user base–i.e. increase the chances of constituents complaining to elected officials on your board if their support for maintaining the data platform and/or your staff running it ever shows signs of wavering.

What we’re talking about is the sweet spot for EDDs: delivery of cost-effective, value-added services that (1) expand resource capacity of member governments, which, hopefully, increases the likelihood of successful implementation of CEDS and other types of plans; (2) create a niche market that presents revenue generating opportunities for EDDs to offer more advanced technical services on a fee-for-service basis; (3) empower community members to get more engaged in planning efforts by lowering the barrier to participate through providing free, publicly-available tools; and (4) grow the EDD’s role in the region.

We thought about these goals a lot when I worked at CAPCOG, the EDA-funded Economic Development District in Austin, but that was more than five years ago and technology (or our knowledge of it) had not improved yet to the point of being able to fully explore where data could take us, at least not in a way that was cost-effective enough to develop a sound business plan around it. Proprietary tools were still too expensive; data still too cumbersome.

Fortunately, we got another chance. East Arkansas Planning and Development District (EAPDD) was awarded a $2.6 million HUD Sustainable Communities grant and then turned to us with a challenge: Create a free, publicly available data platform that the agency could maintain itself with existing staff after the grant money was gone. No licenses. No fees. No maintenance agreements. No consultants, unless they chose to use them, not because they had to. We also had to figure out how community members could be trained to use the data to accomplish planning objectives, and track progress on the new HUD-funded regional and local plans under development.

Here’s an example for economic developers. Suppose you’re working with a prospect company and need to identify a site under a given budget that is zoned commercial with access to ports and rail. The data platform includes a zoning map (where parcel data is available) and you can quickly identify candidate sites based on a company’s infrastructure needs. The data can be viewed on the EAPDD map or downloaded in various file formats, such as Excel or .kml for Google Earth.

Field Guides

No formal training or planning experience is needed to successfully use the field guides, which is one way we are leveling the playing field for small, rural communities. For communities interested in downtown redevelopment, start with learning how to identify an appropriate site and visualize a new development.

Data Dashboard

Effective planning requires an ability to tell a compelling story–helping civic-minded members of your community understand opportunities and challenges and then motivating them to act. EAPDD will use a data dashboard to tell that story at the regional and county level as communities work to implement the new plans.

Designing and building the EAPDD data platform was a challenging experiment and we’re looking forward to seeing how EAPDD and its member communities leverage the new resource to improve the East Arkansas region.